Background to the problem Dutch society demonstrates a development which is apparent in many societies in the 21st century; it is becoming ethnically heterogeneous. This means that children who are secondlanguage speakers of Dutch are learning English, a core curriculum subject, through the medium of the Dutch language. Research questions What are the consequences of this for the individual learner and the class situation?Is a bi-lingual background a help or a hindrance when acquiring further language competences. Does the home situation facilitate or impede the learner? Additionally, how should the TEFL professional respond to this situation in terms of methodology, use of the Dutch language, subject matter and assessment? Method of approach A group of ethnic minority students at Fontys University of Professional Education was interviewed. The interviews were subjected to qualitative analysis. To ensure triangulation lecturers involved in teaching English at F.U.P.E. were asked to fill in a questionnaire on their teaching approach to Dutch second language English learners. Thier response was quantitatively and qualitatively analysed. Findings and conclusions The students encountered surprisingly few problems. Their bi-lingualism and home situation were not a constraint in their English language development. TEFL professionals should bear the heterogeneous classroom in mind when developing courses and lesson material. The introduction to English at primary school level and the assessment of DL2 learners require further research.
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Worldwide, pupils with migrant backgrounds do not participate in school STEM subjects as successfully as their peers. Migrant pupils’ subject-specific language proficiency lags behind, which hinders participation and learning. Primary teachers experience difficulty in teaching STEM as well as promoting required language development. This study investigates how a professional development program (PDP) focusing on inclusive STEM teaching can promote teacher learning of language-promoting strategies (promoting interaction, scaffolding language and using multilingual resources). Participants were five case study teachers in multilingual schools in the Netherlands (N = 2), Sweden (N = 1) and Norway (N = 2), who taught in primary classrooms with migrant pupils. The PDP focused on three STEM units (sound, maintenance, plant growth) and language-promoting strategies. To trace teachers’ learning, three interviews were conducted with each of the five teachers (one after each unit). The teachers also filled in digital logs (one after each unit). The interviews showed positive changes in teachers’ awareness, beliefs and attitudes towards language-supporting strategies. However, changes in practice and intentions for practice were reported to a lesser extent. This study shows that a PDP can be an effective starting point for teacher learning regarding inclusive STEM teaching. It also illuminates possible enablers (e.g., fostering language awareness) or hinderers (e.g., teachers’ limited STEM knowledge) to be considered in future PDP design.
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Within the Netherlands, Content and Language Integrated Learning (CLIL) in foreign language teaching can be considered a sibling of 'Language Oriented Content Teaching' (LOCT), a pedagogy in mainstream classes with second language learners of Dutch, where Dutch is used as language of instruction. This article characterizes two decades of research on LOCT through Dutch in multilingual schools and discusses its relevance for CLIL development.
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The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
